Process variation aware system-level task allocation using stochastic ordering of delay distributions

Design variability due to within-die and die-to-die variations has potential to significantly reduce the maximum operating frequency and effective performance of the system in future process technology generations. When multiple cores in MPSoC have different delay distributions, the problem of assigning tasks to the cores become challenging. This paper targets system level task allocation to stochastically minimize the total execution time of an application on MPSoC under process variation. In this work, we first introduce stochastically optimal task allocation problem. We provide formal theorems of the optimality of the solution in simple scenarios. We extend our theoretical work for generic cases in normal distribution. The proposed techniques enable efficient computation of task allocation using non-stochastic analysis. We apply these techniques in allocating tasks in the embedded system benchmark suites on MPSoC. We show that deterministic solution for system-level task allocation on widely used benchmark topologies and distributions (normal distribution) is almost as good as the best probabilistic solution.